ForceNet is based on message passing GNNs that iteratively update node embeddings based on messages passed from neighboring nodes. In its most general form, the message function depends on the two node embeddings as well as edge features. Many GNN variants fall under this framework [1]}, [2]}, [3]}, [4]}. The GNN-FiLM [5]} uses an embedding of the target node to modulate the message from the source nodes, which is closely related to our expressive message passing architecture. However, most existing message-passing GNNs, including GNN-FiLM, are designed for homogeneous graphs without edge features. Consequently, edge features, which are central to our problem, are often incorporated in an adhoc manner and can be ineffective at approximating complex atomic interactions.
